How Important Is The Matrix Effect in Analyzing Bioprocess Samples?

How important is evaluation of the matrix effects when analyzing bioprocess samples? What are the best approaches to evaluate the effect? And in what situations is the matrix effect acceptable?
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The matrix effect is the effect on an analytical assay caused by all other sample components except the specific compound (analyte) to be analyzed.

Matrix effects are observed either as a loss in response, resulting in an underestimation of the amount of analyte or an increase in response, producing an overestimated result. These effects have long been associated with bioanalytical techniques. However, their evaluation for each assay and each sample matrix can be very time-consuming. Also, these extra evaluation steps bring additional costs.

So, how important is evaluating the matrix effects when analyzing bioprocess samples? What are the best approaches to evaluate the effect? And in what situations is the matrix effect acceptable?

The Importance of Matrix Effect Evaluation

To release final product for use in patients, the product undergoes different quantitative or quantitative analytical procedures. Based on the obtained data, a decision is made to approve or reject the product batch. Therefore, showing that generated analytical data is accurate and no matrix effect is involved is necessary. During the assay development and validation, the impact of the sample matrix is investigated. Each assay is developed to allow monitoring of the matrix effect and, if possible, eliminate it.

Methods to Evaluate and Eliminate

A few quick, qualitative options exist to determine whether a matrix effect is present, such as the dilution-based method. However, this article will focus on a few quantitative methods often used for bioanalytical techniques.

Signal-Based Method

This method allows for quantifying the matrix effect for one specific concentration.

At this concentration, the analyte is measured in the matrix and subsequently measured in a solvent known not to induce any effect. The analyte signal in the matrix is then divided by the analyte signal in the solvent and multiplied by a hundred, resulting in the percentage of matrix effect. If the percentage is below a hundred, the matrix effect results in a suppression of the result. When it is above a hundred, it causes enhancement of the result.

This method is useful when only this concentration is relevant. It doesn’t necessarily provide any indication of other analyte concentrations.

Concentration-Based Method

In this method, the matrix effect is measured with the signal-based method but for a range of analyte concentrations. The concentration-based method is used to show that the matrix effect is not analyte concentration dependent.

Calibration-Based Method

This method is particularly relevant when a blank matrix is not available. Different analyte concentrations are measured in solvent and the matrix, and obtained data are plotted in a graph, and linear regression model is used to generate a slope value.

The slope of calibration analyzed in the matrix is then divided by the slope of the calibration curve prepared in the solvent. The ratio is then multiplied by a hundred to generate %ME. For %ME >100% matrix results in overestimation, and for %ME < 100% tested matrix leads to signal suppression.

When a matrix effect is present, the effect-causing component must be removed from the sample before analysis. Unfortunately, this is not always an option.

In those cases,  matrix minimization (dilution) will provide a way forward. Matrix minimization is especially useful when the analytical technique has sensitivity to spare. Eliminating matrix effect for the particular matrix is proved during the assay development or validation.

When Can You Ignore Matrix Effect?

Theoretically, samples consisting a pure compound could be ignored for matrix testing. However, even a supposedly pure compound may contain other elements in some cases. Elements such as reaction impurities or by-products may lead to matrix effects. Especially during process development, it is challenging to remove the matrix effect. This is due to the large number of matrices generated at each step of upstream or downstream process development.

Analysis of PD samples usually serves to monitor processes and provide process developers with an indication of whether changes in certain process parameters have beneficial or undesirable consequences. As the absolute value of the analysis is less important during the process development compared to batch release analysis, matrix effects are often not completely removed. Instead, they are only monitored using a spike recovery approach. This approach enables sample analysis with sufficient information on a potential matrix effect while saving time and resources.

What are examples of matrix effects?

Matrix effect is predominantly observed in mass spectrometry when there’s a suppression or enhancement of the ionization efficiency of the analyte due to the presence of other compounds in the sample. 

In bioprocessing, several factors can influence and potentially interfere with the accurate detection and quantification of specific proteins or molecules. The presence of salts, lipids, or other organic compounds can mask or distort the detection of certain proteins, and the addition of detergents or buffer components present in a sample can introduce interference during protein quantification, further complicating the process. During the chromatography process, compounds that co-elute may affect the precise quantification of the analyte in focus.

What causes the matrix effect?

The matrix effect arises from a myriad of factors. Primarily, other components in the sample can vie for ionization, potentially overshadowing or altering the analyte’s presence. This competition is further intensified by co-eluting compounds, which can either suppress or amplify the ionization of the analyte. Notably, even subtle changes in the sample’s pH can influence the ionization potential of the analyte, affecting detection and quantification. 

Additionally, chemical interplays between the analyte and the matrix components can introduce deviations. Beyond these chemical influences, disparities in physical properties, like volatility or polarity, between the matrix and the analyte can also contribute to the matrix effect.

What is a matrix effect and how might it impact your results?

A matrix effect describes the changes observed in the detection or quantification of an analyte when other substances are present in the sample. The implications of this phenomenon are manifold. For one, the actual concentration of the analyte may not be accurately represented, leading to reduced precision in results. This is further complicated by decreased sensitivity, where the presence of matrix components can lower the detection limits for the analyte.

Moreover, even if the analyte concentration remains steady, variations in the matrix composition can cause inconsistency between samples, adding to the variability. Additionally, the interference from these matrix components can sometimes result in false positives or negatives, further skewing the detection of the analyte.

How do you determine the matrix effect?

To ascertain the matrix effect, several methods can be employed. One common approach is the Spike and Recovery Method, where a predetermined quantity of the analyte is added to the sample matrix. The concentration is then measured, and the observed value is compared with what was anticipated.

Another technique is post-extraction spiking, which involves comparing the response of an analyte that’s introduced into a sample post-extraction to the response observed when the analyte is added to a pure solvent. Additionally, matrix-matched calibration can be utilized. In this method, the calibration curve derived from an analyte in a matrix-free solvent is juxtaposed against a curve where the standards are calibrated within a sample matrix.

Lastly, a blank matrix analysis can be conducted to scrutinize a sample matrix devoid of the analyte, helping identify potential interference or background signals.

How do you avoid the matrix effect?

To mitigate or altogether sidestep the matrix effect, several strategies can be employed.

One fundamental approach is sample purification, where methods like solid-phase extraction (SPE) or liquid-liquid extraction (LLE) are deployed to refine samples. Additionally, simply diluting samples can help diminish the concentration of interfering matrix components. Further precision can be attained by optimizing chromatographic conditions; tweaking parameters ensures that the analyte doesn’t co-elute with any interfering substances.

Incorporating internal standards can also prove invaluable, as they assist in rectifying variations brought on by the matrix effect. Equally crucial is instrument optimization, where fine-tuning instrument settings can substantially reduce matrix interference. It’s important to note that while it might be challenging to entirely eliminate the matrix effect, a comprehensive understanding and proactive management can significantly bolster the accuracy and reliability of results.


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